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共 124 条记录，以下是11-20 订阅

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Employee Turnover Prediction on Synthetic and Real Datasets

Employee Turnover Prediction on Synthetic and Real Datasets

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international conference on Computer Science and engineering (UBMK)

作者： Azhar Murzaeva Volkan Illik Kadir Yunus Koc R&D Department IBSS Technology and Software Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

When an employee leaves the job, it leads to issues such as financial losses, loss of productivity, re-employment costs, the adaptation period for the new employee, and loss of time. Many studies are dedicated to predicting employee turnover to avoid these negative impacts. In this study, we train classic machine learning models to predict employee turnover. We train Logistic Regression, Decision Tree, XGBoost, Random Forest, Gradient Boosting, Ada Boosting, and Support Vector Machine methods. We use two publicly available datasets: IBM's synthetic data and an anonymous US company's real data. We compare the outcomes of models trained by collected real datasets and models trained by synthetically generated datasets. For the IBM dataset, with 92% accuracy and 0.92 f1-score, Random Forest demonstrates superior results. Additionally, Random Forest out- performed other models with 89% accuracy and 0.89 f1-score for the US company's dataset. All applied classification models demonstrated better results on the synthetic data.

关键词： Support vector machines Productivity Logistic regression Accuracy Computational modeling predictive models Boosting Decision trees Random forests Synthetic data

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Regression Analysis of Ghosting Artifacts in Temporal Fusion Videos Using AutoML

Regression Analysis of Ghosting Artifacts in Temporal Fusion...

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Big Data, Cloud Computing, Data Science & engineering (BCD)

作者： Hyungsub Kim Muneeb A. Khan Heemin Park Department of Software Sangmyung University Cheonan South Korea

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

Temporal Fusion Video (TFV) is a technique that amalgamates multiple frames in temporal sequence to generate a cohesive video output, thereby facilitating various applications such as motion analysis, scene summarization, and the creation of unique visual effects. Despite its advantages in capturing fast-moving objects and efficiently summarizing long-duration changes, TFV is susceptible to ghosting artifacts-transparent overlapping images resulting from frame blending. these arti-facts compromise visual quality and hinder accurate analysis. this paper aims to mitigate the challenge posed by ghosting artifacts by proposing an AI model for quantifying them through measurement and regression analysis, with a specific focus on leveraging Automated Machine Learning (AutoML). Our methodology seeks to identify the key factors contributing to ghosting artifacts and model their impact systematically, thus enabling the development of targeted strategies for artifact reduction. the implications of our research are profound, as it offers a quantitative foundation for evaluating TFV methods, thereby enhancing standardization within the field. Moreover, our approach holds promise for improving the practical applications of TFV in domains such as surveillance, entertainment, and sci-entific visualization. By bridging the existing gap in research, our findings introduce a novel and objective method for enhancing the visual quality and applicability of TFV techniques.

关键词： Surveillance Computational modeling Entertainment industry Standardization predictive models Data science Visual effects Data models Regression analysis Motion analysis

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Photovoltaic Power Forecasting Based on GMM Weather Classification and BiLSTM-ATTENTION

Photovoltaic Power Forecasting Based on GMM Weather Classifi...

引用

international conference on Green Energy and Applications (ICGEA)

作者： Jie Gu Lucheng Hong Yong Zhang Xingrui Zhang Ying Lu Minghe Wu School of Software Engineering Southeast Univeristy Nanjing China School of Electrical Engineering Southeast University Nanjing China Aiswei Technology Co. Ltd. Shanghai China

ISBN: (数字)9798331530778

ISBN: (纸本)9798331530785

the high integration of photovoltaic (PV) systems introduces significant grid instability and randomness to distribution networks. Understanding PV power output is crucial for improving grid stability. To address this, this paper proposes a hybrid PV power forecasting model based on GMM (Gaussian Mixture Model) weather classification. Firstly, PV generation data is normalized and analyzed for correlations. the GMM clustering algorithm is then used to classify the data into three weather types: sunny, rainy, and cloudy. Next, a BiLSTM-Attention neural network is constructed by integrating BiLSTM (Bidirectional Long Short-Term Memory) with an attention mechanism to enhance prediction. Finally, case study comparisons demonstrate that the proposed model can effectively capture local features with high prediction variability under different weather conditions, significantly improving both the accuracy and stability of PV power forecasting.

关键词： Photovoltaic systems Training Accuracy Attention mechanisms Bidirectional long short term memory predictive models Prediction algorithms Classification algorithms Forecasting Meteorology

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G-DPPD: Gated Data-dependent Prior Probability Distribution for Unsupervised Image Captioning

G-DPPD: Gated Data-dependent Prior Probability Distribution ...

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international conference on Image, Vision and Computing (ICIVC)

作者： Xiaoyang Fan Qian Tao Yufei Tang Software Engineering South China University of Technology GuangZhou China Electrical Engineering and Computer Science Florida Atlantic University Boca Raton USA

ISBN: (数字)9798350385991

ISBN: (纸本)9798350386004

Image captioning has become an important research direction in the field of computer vision and natural language processing. However, limited to the expensive cost of labeling paired image-caption data, the task of unsupervised image captioning has attracted increased attention. One of the critical issues or challenges of many previous models is called negative diversity ignorance, where only the correct words were considered when comparing predicted words with ground truth and not considering the difference between incorrect words. In this paper, we propose a novel approach for image captioning, named Gated Data-dependent Prior Probability Distribution (G-DPPD). We make the distribution of the predicted words dynamically approximate the prior and posterior distributions by using a gate mechanism, thus effectively alleviating the above *** and qualitative results on two public benchmark datasets show that our proposed method could significantly improve the performance of unsupervised image captioning.

关键词： Maximum likelihood estimation Costs Correlation Computational modeling Logic gates Benchmark testing predictive models Probability distribution Natural language processing Labeling

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Pasture Biomass Research Using Unmanned Aerial Vehicle

Pasture Biomass Research Using Unmanned Aerial Vehicle

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Energy Efficiency and Agricultural engineering (EE&AE), international conference on

作者： Durhan Saliev Iliyan Damyanov Kalin Dimitrov Georgi Mladenov Department of Combustion Engines Automobile Engineering and Transport Faculty of Transport Technical University of Sofia Sofia Bulgaria Department of Radio communications and Video technologies Faculty of Telecommunications Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350369380

ISBN: (纸本)9798350369397

Unmanned aerial vehicles are using often on research in deferent areas. Pasture research is one of them and include data brings and analysis for many of parameters that give an information about grassy, biomass, slope of terrain etc. this study present similar data analysis that bring with drone at area designed for cattle pasture in Rodopa Mountain in Bulgaria. Studied parameter is volume with grass vegetation using photogrammetry. Result obtained covered three-year study period, from 2021 to 2023 year. Two methods have been used to analyze the volume - Digital Surface Model (DSM) and Digital Terrain Model (DTM), comparing that which must be added or removed to level the surface with the surrounding land. the research and analysis of the results showed it requires additional researches to applicate of the presented method for determining the volume and amount of grass for low-height grazing on designated pastures.

关键词： Solid modeling Vegetation mapping Land surface predictive models Autonomous aerial vehicles Reliability engineering software

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Leveraging predictive Maintenance for Photovoltaic Systems 9th

Leveraging Predictive Maintenance for Photovoltaic Systems

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9th international conference on Smart City Applications, SCA 2024

作者： Ledmaoui, Younes Raif, Mourad El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

ISBN: (纸本)9783031886522

this paper introduces a novel edge device architecture designed to optimize solar energy management systems. It integrates cutting-edge functionalities such as generation prediction, maintenance alerts, and anomaly detection into a unified framework. through the utilization of edge computing, the system enables real-time analysis and decision-making at the network’s edge. Leveraging machine learning algorithms and predictive models, these edge devices provide highly accurate energy generation forecasts, facilitating efficient energy utilization and strategic planning. Additionally, the architecture incorporates anomaly detection techniques to proactively identify deviations from normal operation, minimizing downtime and enabling timely maintenance interventions. the seamless integration of these features within the edge devices significantly enhances the overall efficiency, reliability, and performance of energy monitoring systems. By adopting this advanced edge device architecture, energy stakeholders can reap benefits such as improved energy management, reduced operational costs, and enhanced system reliability. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Battery management systems

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A Random Forest Based Prediction Method for Moisture Content in Wood Materials

A Random Forest Based Prediction Method for Moisture Content...

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IEEE international conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Yan Xu Qing Ye Wei Song School of Software & Internet of Things Engineering Jiangxi University of Finance and Economics Nanchang China School of Information Management Dalarna University Dalarna Sweden

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

Missing values is a crucial problem in the area of big data analysis, which hinders data integrity. Various regression methods have been employed for estimating missing values, but they exhibit significant prediction errors. To ensure the integrity of data collected from a wood sensor monitoring system and address the issue of data loss and anomaly, we propose a missing value estimation method based on the random forest regression model. this study focuses on the environmental data, including temperature, relative humidity, and absolute humidity surrounding the wood subjects. We simulate a number of methods on the data for comparison purpose. the experiment results in terms of prediction performance indicate that the random forest regression model algorithm we developed for estimating moisture content's missing values yields favourable outcomes with consistently low estimation errors.

关键词： Radio frequency Temperature sensors Accuracy Data integrity Moisture Humidity predictive models

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Exploring Machine Learning models to Predict the Diamond Price: A Data Mining Utility Using Weka

Exploring Machine Learning Models to Predict the Diamond Pri...

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international conference on Advanced Computing and Communication Systems (ICACCS)

作者： Md Shaik Amzad Basha M Martha Sucharitha A. Uma Devi M Ashok Peerzadah Mohammad Oveis GITAM School of Business Gandhi Institute of Technology and Management (Deemed to be University) Bengaluru India Department of Professional Studies Christ Deemed to be University Bengaluru India Department of Management Studies SRM Valliammai Engineering College Chengalpattu India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India

In contrast to gold and platinum, whose values may be fairly determined, determining a diamond9;s worth involves a far more complex set of considerations. the appropriate rate is based on many factors, not just one... 详细信息

In contrast to gold and platinum, whose values may be fairly determined, determining a diamond's worth involves a far more complex set of considerations. the appropriate rate is based on many factors, not just one of the stones. Diamonds are graded based on their appearance, carat weight, cut quality, and how well they have presented dimensions like a table's surface, depth, and breadth. In order to accurately forecast diamond prices, this study seeks to develop the most effective approaches possible. Different machine learning classifiers are trained on the diamond dataset to forecast diamond prices based on the features. this article shows how to analyze diamond prices using WEKA's data mining software. Diamond data have been utilized for this study. these methods include M5P, Random Forest, Multilayer perceptron, Decision Stump, REP Trees, and M5Rules. For the purpose of estimating the cost of a diamond, different Machine Learning classifiers are compared and contrasted. Performance measures and analysis showed that Random Forest was the best-performing classifier. Experimental findings show, as shown by the coefficient of correlation that Random Forest is better than other classification methods.

关键词： Analytical models Platinum Diamonds Pricing predictive models Multilayer perceptrons Data models

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An External Method of Estimating Lateral Forces for a Da Vinci Xi Surgical Instrument

An External Method of Estimating Lateral Forces for a Da Vin...

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international conference on Mechatronics (ICOM)

作者： Christalline Jhine L. Shi Ralph Gerard B. Sangalang Lowell Nathaniel B. Sinason Chuang Cheng-Hsin Nilo T. Bugtai Department of Manufacturing Engineering and Management De La Salle University Manila Philippines Electronic Systems Research Center Batangas State University-The National Engineering University Batangas City Philippines Department of Electronics Engineering Batangas State University - The National Engineering University Batangas City Philippines Institute of Medical Science and Technology National Sun Yat Sen Universty Kaohsiung Taiwan

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

this study presents a comprehensive investigation into the development of an external method for estimating lateral forces at the custom trocar clamp of the da Vinci Xi surgical robot arm. the research focuses on the mechanical, electrical, and software design aspects involved in data collection, detailing the meticulous attention paid to strain gauge placement and the integration of custom strain modules. A detailed analysis of the collected data is conducted, emphasizing the importance of data preprocessing techniques such as alignment, truncation, and smoothing. the study highlights the challenges encountered and the strategies employed to overcome them, leading to the derivation of discrete-time state-space models tailored to voltage-force and strain-force data. the performance of various model forms and identification methods, including N4SID and SSEST, is evaluated, with training fit reaching 95.49% and validation fit at 93.55%, demonstrating high accuracy and generalization capability. the findings underscore the importance of considering feedthrough components in model development, and the study contributes valuable insights into the integration of force sensing technologies into surgical robotics. these advancements pave the way for enhanced surgical precision and safety, ultimately benefiting patients and healthcare providers alike.

关键词： Training Smoothing methods Accuracy Force predictive models Robot sensing systems Strain measurement

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Implementation of Expert Knowledge in the Development of Diagnostic Systems for High-Speed Marine Diesel Engines

Implementation of Expert Knowledge in the Development of Dia...

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international Multidisciplinary conference on Computer and Energy Science (SpliTech)

作者： Petar Vrvilo Tino Vidovi Liane Roldo Gojmir Radica Faculty of Martime studies University of Split Split Croatia Mechanical Engineering and Naval Architecture Faculty of Electrical Engineering University of Split Split Croatia

ISBN: (数字)9789532901351

ISBN: (纸本)9798350390797

Real-time expert systems for diesel engine diagnostics would represent sophisticated software systems that compare factory values obtained by continuous measurement of the diesel engine under test with real-time data obtained from sensors on the engine. the objective of this study is to present the results of a virtual model of the observed propulsion system that can be used to diagnose and predict potential unplanned system shutdowns. the mentioned systems employ sophisticated algorithms to identify deviations from optimal operating parameters, discern potential faults or issues in engine operation, and forecast potential future malfunctions. Based on expert rules and heuristics, this part of the system utilizes the results of data analysis to draw conclusions about the engine's condition and identify potential problems or faults. the results of diagnostic analyses are presented to users through interfaces, typically in the form of graphs, tables, or alarm messages. Detailed reports on the engine's condition and maintenance recommendations are also generated. these systems could be integrated with maintenance management systems, enabling automatic generation of work orders to address identified problems and tracking of work execution. the combination of these components enables the creation of an intelligent system that continuously monitors, diagnoses, and manages diesel engine operation in real time, increasing reliability, safety, and efficiency of maritime operations.

关键词： Fault diagnosis Analytical models Diesel engines Atmospheric modeling predictive models Real-time systems Production facilities

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